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Thirty Minutes of Mobile Throttling in Northern Taiwan, Read from OONI's Public Data

Measuring the mobile throttle of 13 August together

Taiwan has long gone without large-scale throttling or shutdowns. That is good for daily life, and for anyone trying to understand network anomalies it means there is no local sample to compare against. OONI is a long-running open source project measuring network interference and connection quality worldwide; anyone can install its app to contribute, and every result is published. Taiwan's record in that database has consistently shown a network working normally.

In the 30 days before 13 August, the three mobile carriers in Taiwan recorded 564 successful connection speed tests (the test is named ndt), with a median download of 12,425 kbit/s and only 4 measurements below 2,000 kbit/s, two of which were failures. kbit/s counts thousands of bits transferred per second — higher is faster, and 1,000 kbit/s is roughly 1 Mbps.

From 14:30 to 15:00 Taipei time on 13 August, mobile networks across seven northern counties were throttled for 30 minutes. Two days earlier the community put out a call asking people to run OONI Probe's performance tests during that window. Taiwan recorded 238 connection speed tests and 235 video streaming tests (named dash) that day, with 170 and 168 of them completed on mobile networks, coming from 47 devices. During the equivalent window at the central Taiwan drill on 10 August, the whole country produced zero mobile performance measurements.

The call started with community member mashbean, who proposed using the drill as a rare measurement opportunity. The two sides then prepared separately, one writing the call and the operating instructions, the other observing in the field on the day. He wrote his own record up as a separate post, which follows the network past the announced end of the drill and covers a period this dataset cannot see. Read together, the two posts span the full afternoon.

One Chunghwa Telecom Mobile handset measured 788 to 1,709 kbit/s during the throttle. Taiwan's public data had never shown figures of that order. It should be said up front that those figures come from 6 records left by a single device: enough to stand as one complete case, not enough to generalise to the country or to any carrier.

This post records network conditions. Whether carriers followed instructions precisely, and whether the officially published figure is accurate, are both outside its scope. Taiwan had no data on what throttling looks like, and preserving these 30 minutes is the point. Every query below works without authentication or an API key, so readers can run them again.

The most useful thing to come out of the analysis has nothing to do with throttling itself. It is a reading method: throttling and blocking produce completely different shapes in OONI data. Blocking sends anomaly rates up and leaves confirmed-blocking records behind, while throttling leaves the verdict fields untouched and shows up only in throughput and latency values. Taiwan's web connectivity anomaly rate that day was 1.0%, in the same band as the usual 0.6%, with confirmed blocking at 0 all day. Only speed went down. Describing a throttle through blocking anomaly rates returns a figure close to normal, and leads to the conclusion that nothing happened.

Recording 30 Minutes of Northern Taiwan's Mobile Networks During an Announced Throttle on 13 August

Measuring Taiwan's mobile network throttling drill

On Thursday 13 August, from 14:30 to 15:00 Taipei time (UTC+8), mobile networks in Keelung, Taipei, New Taipei, Taoyuan, Hsinchu City, Hsinchu County, and Yilan will be throttled for thirty minutes. The throttle is part of Taiwan's Urban Resilience Exercise, executed simultaneously by all three major carriers. Official announcements state that voice calls, SMS, and cell broadcast continue to work, and that high-bandwidth services degrade: video streaming, video calls, mobile payments, cloud sync.1 The advice for the window from the NCC (National Communications Commission, Taiwan's telecom regulator) is to prepare in advance for going offline.2

Officials have described the depth of the throttle twice. On 20 July, Defence Minister Wellington Koo told the Legislative Yuan (Taiwan's parliament) that speeds would drop to one percent of 4G and 5G capacity. After the central Taiwan drill on 10 August, the Executive Yuan (Taiwan's cabinet) described the method: carriers apply rate limiting in the core network, capping mobile download speeds at 256KB. The two statements are of the same order, and either one gives a figure to expect when the measurements come in.

The date, the window, the seven counties, the three carriers: every boundary of this throttle is public before it runs. Studies of network throttling rarely get that. In most cases users notice slowness first and researchers reconstruct the timeline afterwards, leaving the edges fuzzy. Pre-announced shutdowns are not new elsewhere, exam-period national shutdowns being the familiar example.6 On the publicly documented record, a pre-announced throttle is less common, particularly one with per-carrier granularity.

During the same window at the central Taiwan drill on 10 August, Taiwan's OONI observations contained no performance measurement from a mobile network at all. The northern drill is the last of this year's exercise, so after 13 August the opportunity does not come round again.

If you are in one of those seven counties, your phone is going to slow down for that half hour regardless. Rather than just waiting it out, you might as well leave a record behind.

Defending the Public's Right to Know: A Sinophone Asia-Pacific Reading of OONI in Practice

This post sits alongside Tor Project's spotlight series article on OONI. The body below summarises what the original covers and then maps the same workflow onto Sinophone Asia-Pacific — the section the original does not cover, and the main contribution of this post.

For the full narrative on Kenya's High Court case, Tanzania's follow-up litigation, Meduza's public-education framing, and the supporting cases from Egypt, Jordan, and India, please read the original:

Visual header for the Tor Project 'Defending the free internet' spotlight series, featuring OONI
Image source: Tor Project Blog.

Editorial note: This is a companion post for English-speaking readers. The original article, by Maria Xynou (OONI), was published on the Internet Society Pulse blog and reposted on OONI's blog. Please read it there. anoni.net Docs is reposting a short summary here, alongside translations into Traditional Chinese and Simplified Chinese, plus a regional context section for readers in Chinese-reading regions.

What the original article covers

Maria Xynou's post is a concise overview of how Internet censorship is becoming harder to measure, and what OONI's data shows about how it is evolving. The main points:

  • Censorship is rarely binary: Confirming a block is not just "up" or "down". False positives are common, methods range from DNS manipulation and IP blocking to throttling and forged responses, and the same site may be blocked on one network but reachable on another.
  • OONI's approach: Open measurement methodologies, control measurements, a probabilistic pipeline (OONI Pipeline v5) that classifies results as "blocked", "down", or "OK" with confidence estimates, and crowdsourced data from volunteers running OONI Probe on their local networks.
  • Seven evolving trends: Globalization and normalization of censorship beyond the usual suspects, short-term targeted blocks around political events, long-term systemic suppression of marginalised communities, less transparent censorship in an encrypted web (TLS interference via DPI, no block pages), throttling and degraded service as subtler control, attacks on privacy technologies (encrypted DNS, ECH), and the rise of national intranets and allowlisting approaches.
  • Measurement enables advocacy: OONI has been part of the #KeepItOn campaign since 2016, and OONI data has supported legal and policy interventions in Gabon, Tanzania, Nepal, Togo, Mozambique, Pakistan, Kenya, and others.
  • Scale: Over 3 billion measurements from 30,000 networks across 245 countries and territories since 2012, with tens of millions of new measurements added every month.

Please read the original for the full argument, all the supporting links, and the country-specific examples:

OONI is guarding its data against bad measurements — what that means if you build on it

We build on OONI's public dataset. Our own work tracks how well Taiwan and the wider APAC region are actually observed in that data, and our Run v2 census mapped how the whole Run v2 ecosystem gets used. So when OONI published a long engineering post on [how it detects and mitigates faulty measurements]1 — alongside a new anonymous-credential system now rolling into production — we read it not as OONI insiders but as people downstream who use this data to make claims about a thinly-observed part of the world.

Here is what stands out from that seat.

OONI Run v2 usage census

Worldwide, OONI Run v2 has produced 14.17 million measurements, and just three lists account for 72% of them. The highest-volume lists all work the same way: each one targets a single censorship or blocking phenomenon, and a continuously-running measurement backend executes it on a schedule, accumulating data over time. We surveyed every Run v2 link to measure how concentrated this is, and to draw out what the pattern offers communities that want to run their own local connectivity observation.

OONI (the Open Observatory of Network Interference) is a global censorship-measurement project. Its mobile app, OONI Probe, runs through a list of websites and reports whether each one is reachable from where you are. OONI Run v2 lets anyone compose their own list of sites to watch, generate a link, and have others run that list with one tap in OONI Probe, with every result flowing into OONI's public dataset. You can define your own measurement targets without writing code, yet few people know the feature exists or have used it, which is exactly why we wanted to see how it is actually used.